• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.57-58
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• 2019

본 논문에서는 앞서 진행한 연구들과 딥러닝을 이용한 고소작업자 행동 모니터링 논문에 이어 작업자 위험 행동분류 시스템을 개선할 수 있는 연구 결과를 비교, 설명한다. 이번 연구에서는 작업자의 행동에 따른 고도계 센서의 데이터를 추가로 수집하여 작업자의 더 다양한 행동을 분류하고 위험 행동 패턴 분석을 위한 방향을 제시한다.

• Journal of the Korea Society of Computer and Information
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• v.23 no.7
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• pp.113-120
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• 2018

The purpose of this study is to derive implications for effective management of the public social worker by investigating the relationship between social support, job attitude and self-esteem of the public social worker. In order to accomplish this research purpose, social support of the public social worker as independent variables, job attitude as a dependent variable, and self-esteem as a parameter were analyzed and the relationship between these variables was analyzed. As a result, emotional, evaluative, material, and informational support, which constitute the social support of the public social worker, have a significant effect on job attitude, and self-esteem has a mediating effect on the relationship between these variables. Based on the results of this analysis, the importance of social support of the public social worker was suggested.

• Journal of the Korea Society of Computer and Information
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• v.23 no.2
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• pp.63-70
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• 2018

Safety management agent manages the risk behavior of the worker with the naked eye, but there is a real difficulty for one the agent to manage all the workers. In this paper, IoT device is attached to a harness safety belt that a worker wears to solve this problem, and behavior data is upload to the cloud in real time. We analyze the upload data through the deep learning and analyze the risk behavior of the worker. When the analysis result is judged to be dangerous behavior, we designed and implemented a system that informs the manager through monitoring application. In order to confirm that the risk behavior analysis through the deep learning is normally performed, the data values of 4 behaviors (walking, running, standing and sitting) were collected from IMU sensor for 60 minutes and learned through Tensorflow, Inception model. In order to verify the accuracy of the proposed system, we conducted inference experiments five times for each of the four behaviors, and confirmed the accuracy of the inference result to be 96.0%.

• Proceedings of the Korean Society of Computer Information Conference
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• 2017.07a
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• pp.259-260
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• 2017

본 논문에서는 건설 현장이나 발전소 등의 고소 작업이 많은 곳에서 작업하는 근로자의 안전을 확보하기 위해, 클라우드 웹 서비스에 기반에 고소작업자의 행동 데이터를 수집 저장하여 그 데이터를 통해 관리자가 작업자의 행동을 모니터링 하고 위험경고 메시지를 받을 수 있는 시스템을 제안하였다. 작업자가 하는 행동을 관리자가 실시간으로 확인하는 것을 통해 고소 작업산업 현장에서 작업자의 경각심으로 예방이 가능하다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.07a
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• pp.25-26
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• 2018

이 논문에서는 고소 작업자의 위험 행동 분석을 위해 딥러닝 기법 중 연속적인 데이터 분석에 적합하며 매우 뛰어난 성능을 보여주는 LSTM 알고리즘을 이용해 모니터링 하는 시스템을 개발하였다. 모델을 위해 학습 데이터는 안전벨트에 자이로센서 등을 부착해서 실험하였다. 시스템은 작업자의 5가지의 행동 패턴을 분석할 수 있으며, 96%의 정확도를 얻었다.

• Journal of the Korea Society of Computer and Information
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• v.23 no.6
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• pp.67-73
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• 2018

The purpose of this study is to investigate the relationship between interpersonal stress and job burnout, turnover intention of social welfare worker's in social welfare center and efficient response of welfare worker's. In order to accomplish this study purpose, the interpersonal stress of social welfare worker's in social welfare center as an independent variable, interpersonal stress as a dependent variable of job burnout and turnover intention, and stress coping ability as a moderating variable were selected. The causal relationship between interpersonal stress and job burnout, turnover intention and the moderating effect of stress coping ability were analyzed. As a result of the analysis, the interpersonal stress of social welfare worker's showed a significant effect on job burnout and turnover intention. Also, moderating effects of stress copying ability were significant. Based on the results of this analysis, the theoretical implications and policy implications of this study are suggested, and the directions and limitations of this study are suggested.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.4
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• pp.135-142
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• 2021

Estimating the locations of workers in a shipyard is beneficial for a variety of applications such as selecting potential forwarders for transferring data in IoT services and quickly rescuing workers in the event of industrial disasters or accidents. In this work, we propose a human movement stream processing system for estimating worker locations in shipyards based on Apache Spark and TensorFlow serving. First, we use Apache Spark to process location data streams. Then, we design a worker location prediction model to estimate the locations of workers. TensorFlow serving manages and executes the worker location prediction model. When there are requirements from clients, Apache Spark extracts input data from the processed data for the prediction model and then sends it to TensorFlow serving for estimating workers' locations. The worker movement data is needed to evaluate the proposed system but there are no available worker movement traces in shipyards. Therefore, we also develop a mobility model for generating the workers' movements in shipyards. Based on synthetic data, the proposed system is evaluated. It obtains a high performance and could be used for a variety of tasksin shipyards.

• Journal of the Korea Society of Computer and Information
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• v.20 no.9
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• pp.113-119
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• 2015

The purpose of tis study is to analyze the effects of job embeddedness on job burnout related with social worker's turnover. This study suggests some implications for decreasing the level of social worker's job burnout. In this study, embeddedness is composed of three sub-variables like fit, links, and sacrifice. Job burnout is also composed of emotional exhaustion, depersonalization, and decrease of personal accomplishment. Thereafter, research model is established, and study hypothesis is tested through the survey. As a result, it showed that the components of job embeddedness have significant effects on the components of job burnout. Based on the result of this empirical analysis, this study suggested some theoretical and political implications.

• Journal of the Korea Society of Computer and Information
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• v.20 no.11
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• pp.169-174
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• 2015

The study is to clarify the relationship among the positive and negative recognition of stress and physical and psychological abuse and neglect aiming at getting the material. They are to prevent elder abuse at the main care worker for frail and dementia elderly. The degree of fitness to the data where positive and negative recognition of main care worker was located as dependent variable. The casual model in which main care worker was located as independent variable. The degree of fitness of casual model was GFI=0.772, CFI=0.795, RESEA=0.067. Among path coefficient included in the previous model, three of figures going toward three of abuse to the elder were statistically significant.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.353-360
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• 2022

Due to the dense and complicated working environment, the construction industry is susceptible to many accidents. Worker's fall is a severe problem at the construction site, including falling into holes or openings because of the inadequate coverings as per the safety rules. During the construction or demolition of a building, openings and holes are formed in the floors and roofs. Many workers neglect to cover openings for ease of work while being aware of the risks of holes, openings, and gaps at heights. However, there are safety rules for worker safety; the holes and openings must be covered to prevent falls. The safety inspector typically examines it by visiting the construction site, which is time-consuming and requires safety manager efforts. Therefore, this study presented a worker-driven approach (the worker is involved in the reporting process) to facilitate safety managers by developing integrated computer vision and inertia sensors-based mobile applications to identify openings. The TensorFlow framework is used to design Convolutional Neural Network (CNN); the designed CNN is trained on a custom dataset for binary class openings and covered and deployed on an android smartphone. When an application captures an image, the device also extracts the accelerometer values to determine the inclination in parallel with the classification task of the device to predict the final output as floor (openings/ covered), wall (openings/covered), and roof (openings / covered). The proposed worker-driven approach will be extended with other case scenarios at the construction site.